The diagnosis of brain tissue anomalies is an ongoing challenge in modern medicine. Because of the location of the brain within the skull, and the sensitivity of brain tissue to invasive procedures, the diagnosis of suspected brain disease balances the need for timely and accurate diagnosis with the need to minimize damage to the brain tissue in the course of performing diagnostic procedures. The risk of adverse outcomes related to the diagnostic procedures is significant. For example, in 7,500 brain biopsy procedures conducted from 1979 to 1991, the diagnostic accuracy was 91%. However, the morbidity and mortality rates associated with the invasive biopsy procedure were 3.5% and 0.7%, respectively.
In addition, early detection leading to early intervention in a brain anomaly is an important consideration. However, due to the dangers associated with biopsy in a situation of recognized tissue anomaly, early detection is typically achieved using non-invasive procedures such as computed tomography (CT) or magnetic resonance imaging (MRI). However, existing non-invasive diagnostic procedures are relatively ineffective at detecting the early stages of a brain anomaly. Based on non-invasive procedures such as CT or MRI scans alone, practitioners cannot consistently distinguish radiation necrosis or benign lesions from malignant tumors.
Magnetic resonance spectroscopy (MRS) with pattern recognition has recently shown potential for the non-invasive diagnosis of brain lesions, for direction of surgical or other therapeutic interventions, and for determining prognosis. Patterns indicative of abnormal tissue appear in brain MRS scans before any abnormality is indicated in the corresponding MRI scans. Therefore, use of reliable MRS-based patterns could enable earlier detection of brain tissue anomalies. Further, if the MRS scans contain sufficiently distinctive and reliable markers, MRS diagnostic procedures could augment or substitute for histological grading, guide surgical intervention at tumor margins and areas of local invasion, and monitor radiation or chemo-therapy progress. Whether these potential advantages are realized depends on the predictive quality of computational models based on MRS scan data that in turn depend on the consistency and inherent informational content of the MRS scans.
Previous research on a variety of tissue types indicates that 1.5 Tesla (T) MRS scans contain information useful for diagnostic purposes, particularly biomarkers for AT-acetyl aspartate (NAA), choline (Cho), creatinine (Cr), myo-inositol (MI), lactate, and lipids. For example, using an automated two-category classification model, MRS diagnostic procedures achieved a sensitivity of 80% and specificity of 86% for discriminating breast cancer tissue from scar tissue. However, in brain or other tissues, simple biomarker ratios used for diagnosis such as Cho/Cr cannot adequately distinguish malignant lesions from other features such as progressive multifocal leukoencephalopathy, multiple sclerosis, stroke, and scar tissue.
The detection of brain lesions using MRS requires recognizing patterns in MRS scans associated with abnormalities. One method involves the interpretation of MRS scan patterns by an expert practitioner. When an expert interprets MRS spectra for diagnostic purposes, he/she may consider the relative amounts or ratios of two or three biomarkers such as Cho/Cr. Spectrum scans are visually examined, peak biomarkers are identified, and peak heights are scaled manually to determine these ratios. MRS peaks, even if not precisely at the expected chemical shift, may be recognized for the biomarker they represent by the relationship of the biomarker peak to the familiar pattern of peaks in the MRS scan.
Diagnostic pattern recognition on the whole MRS spectrum, rather than classification based on a few biomarker ratios, is a recent development. The effective use of multiple biomarkers for diagnosis requires not expert, but automated peak identification and quantification. For example, a multicenter study evaluated automated classification of tumors based on proton MRS patterns using linear discriminant analysis and leave-one-out validation to achieve greater than 90% correct classification of the tissue type in scans from multiple instruments and centers. However, the classification scheme of this study was limited to only three types of tumors. In routine practice, the accurate diagnosis of many different types of tumors as well as normal tissue and other types of non-cancerous anomalies is highly desirable. This goal may be achieved using MRS scans of normal tissue and other types of non-cancerous anomalies in the training and external validation sets of an automated diagnostic pattern recognition method. The accuracy and utility of predictive models strongly depend on at least several factors related to the quality of the MRS scans such as the reproducibility of chemical shifts for each biomarker and the potential for confusion among the spectrum classes modeled.
For example, spatial variation in Bo, the magnetic field strength of the MRS devices, may result in the misalignment of biomarker chemical shifts. The magnetic field strength varies as a function of target tissue depth. Although the scale of the variations in chemical shift is not so great as to cause misidentification of markers under expert interpretation, the same is not true when automated detection of many different biomarkers is required for computerized pattern recognition. Misidentification of biomarkers between data replicates confounds the MRS-based models and effectively decreases peak resolution by spreading the domain over which each diagnostic feature may appear. For example, FIG. 1 shows an example of the variation in biomarker chemical shifts among several spectra measured from the same tissue type.
The non-uniformity of biomarker peaks represents a significant limitation in use of automated MRS-based pattern recognition. High-magnetic-field medical MRI devices produce MRS scans that have greater biomarker peak resolution and therefore superior ability to distinguish proximate, but diagnostically distinct, spectrum features. For example, using a high-powered 8.5 T MRS device to analyze tissue samples from fine needle biopsies, breast tissue anomalies were detected with nearly 100% correct categorization and sensitivity for the extent of breast tissue anomalies. However, the high-powered MRS instruments are not nearly as ubiquitous as 1.5 T MRS instruments in the medical community.
Another approach to enhancing the quality of MRS data is the preprocessing of the raw MRS data prior to analysis. Data preprocessing is a well-known tool used in research fields that utilize high volumes of data containing both signal and noise, such as mass spectrometry, genomics, proteomics, metabolomics, and structure-activity relationships. Common preprocessing techniques typically include normalization, baseline correction, various kinds of weighting, smoothing, variance or other kinds of scaling, and a priori information weighting. In all computational modeling, such as that used for MRS pattern recognition, there is a concern that preprocessing may obscure the informational content of the raw data; validation of the model and its predictions is important in order to assess the value of preprocessing steps.
Previous research has highlighted the beneficial effects of preprocessing MRS data using normalization and digitization on the diagnostic outcome of predictive diagnostic models. However, research to date has overlooked the importance of re-calibrating MRS spectra to account for Bo variations in order to realign the chemical shifts as a critical first step in data preprocessing. A need exists in the art for a preprocessing method that minimizes variation in chemical shifts and enhances the resolution of biomarker shifts relative to random noise. Due to the higher consistency of the preprocessed MRS relative to the raw MRS scans, higher resolution of tissue types including healthy tissue, non-malignant tumors, and different types of tumors could be achieved. Further, the higher fidelity signal even when the MRS signal is relatively weak resulting from preprocessing would make possible the earlier detection of tissue anomalies. In addition, preprocessing of MRS data measured using the more commonly available lower-powered MRS devices would make the enhanced diagnostic methodologies using automated MRS pattern detection more widely available.